Computer Science, asked by bijukumarmnj1382, 11 months ago

How to convert discrete grouped data to continuous?

Answers

Answered by Anonymous
0

Answer:

hii

your answer is here !

Explanation:

I second some of the caution advised in using this approach. That being said, you can certainly force the distributions for each letter to appear smooth. One option in R is using the ggplot function stat_density where you can specify a bandwidth bw= that governs the degree of smoothing. Of course smoothing reduces your information, and you will have to decide the appropriate trade off between smoothing and information loss when visually representing your distributions.

Using the iris data in R here is one approach that you can build from.

follow me !

Answered by dhayadon
0

Explanation:

Heya!

1) Because I am a novice when it comes to reporting the results of a linear mixed models analysis, how do I report the fixed effect, including including the estimate, confidence interval, and p-value in addition to the size of the random effects. I am not sure how to report these in writing. For example, how do I report the confidence interval in APA format and how do I report the size of the random effects?

2) How do you determine the significance of the size of the random effects (i.e. how do you determine if the size of the random effects is too large and how do you determine the implications of that size)?

3) Our study consisted of 16 participants, 8 of which were assigned a technology with a privacy setting and 8 of which were not assigned a technology with a privacy setting. Survey data was collected weekly. Our fixed effect was whether or not participants were assigned the technology. Our random effects were week (for the 8-week study) and participant. How do I justify using a linear mixed model for this study design? Is it accurate to say that we used a linear mixed model to account for missing data (i.e. non-response; technology issues) and participant-level effects (i.e. how frequently each participant used the technology; differences in technology experience; high variability in each individual participant's responses to survey questions across the 8-week period). Is this a sufficient justification?

I am very new to mixed models analyses, and I would appreciate some guidance.

Hope it helps!

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